Accelerated signal and image modeling methods and devices are well known. Some known traditional acceleration methods are based on transformation signal and image modeling schemes that approximate optimum transformation schemes commonly referred to as the Karhunen-Loeve transform (“KLT”). For example, the discrete cosine transform (“DCT”) is such an acceleration scheme that has found widespread application in diverse areas such as image compression where it is the basis of international standards such as JPEG and MPEG. These standards decompose the images into low dimensional, 8×8, picture element (“pixel”) blocks for their fast transformation and subsequent compression. However, these acceleration schemes are limited to transformation models, and generally do not work as well when applied in more general modeling frameworks that combine both prediction and transformation.
For example, in image compression applications, a methodology integrating prediction and transformation models and using an optimum minimum mean squared error (“MMSE”) criterion has been found to significantly improve the blocking effects that result from transformation methods such as the DCT that do not use prediction to exploit the correlation that exists between encoded pixel blocks. An image compression method based on this MMSE PT modeling methodology was filed on Oct. 22, 2000 as U.S. patent application Ser. No. 09/696,197 entitled “Super Predictive-Transform Coding” (the '197 application), the disclosure of which is herein incorporated by reference in its entirety. The '197 application has been found to significantly outperform the best image compression techniques available at the present time, including both DCT and wavelet based compressors. The key to the high performance of the aforementioned invention is a MMSE PT signal and image model superstructure that forms the basis of the proposed method and apparatus. The MMSE PT model consists of prediction and transformation matrices that result from the solution of coupled eigensystem and normal design equations.
In addition to image compression, the modeling technique can also be incorporated in other signal and image processing applications such as estimation, detection, identification, channel and source integrated (“CSI”) coding, control and other related areas. See for instance Feria, E. H., “Predictive-Transform Estimation”, IEEE Transactions on Signal Processing, November 1991 (the “1991 IEEE Trans. On Signal Processing paper”)and also Feria, E. H. “Decomposed Predictive-Transform Estimation”, IEEE Transactions on Signal Processing, October 1994, the disclosure of both which is herein incorporated by reference in their entirety, where it was shown that a MMSE PT signal model can be used to generate a new class of estimators that has as special cases classical Kalman and Wiener estimators and that in addition leads to very simple decomposed structures. In two other publications Guerci, J. R. and Feria, E. H. “On a Least Squares Predictive-Transform Modeling Methodology,” IEEE Transactions on Signal Processing, July 1996, and Guerci, J. R. and Feria, E. H. “Application of a Least Squares Predictive-Transform Modeling Methodology to Space-Time Adaptive Array Processing,” IEEE Transactions on Signal Processing, July 1996, the disclosure of both is herein incorporated by reference in their entirety, demonstrate how the MMSE PT signal model forms the basis of a more general adaptive signal modeling strategy that has widespread applications. In Feria, E. H. “Predictive Transform: Signal Coding and Modeling,” Eleventh Triennial World Congress of IFAC, Tallinn (Estonia, USSR), Oxford: Pergamon Press, August 1990, the disclosure of which is herein incorporated by reference in its entirety, the technique was applied to the modeling of control processes or plants.
One problem with traditional methods of prediction and transformation is that the tend to be slow. Additionally, the computational burden of traditional methods was excessive.
These and other deficiencies in the methods for traditional accelerated signal and image modeling are addressed by the present invention.